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We describe an equilibrium model of peer-to-peer product sharing, or collaborative consumption, where individuals with varying usage levels make decisions about whether or not to own a homogeneous product. Owners are able to generate income from renting their products to nonowners while nonowners are able to access these products through renting on an as-needed basis. We characterize equilibrium outcomes, including ownership and usage levels, consumer surplus, and social welfare. We compare each outcome in systems with and without collaborative consumption and examine the impact of various problem parameters. Our findings indicate that collaborative consumption can result in either lower or higher ownership and usage levels, with higher ownership and usage levels more likely when the cost of ownership is high. Our findings also indicate that consumers always benefit from collaborative consumption, with individuals who, in the absence of collaborative consumption, are indifferent between owning and not owning benefitting the most. We study both profit-maximizing and social-welfare–maximizing platforms and compare equilibrium outcomes under both in terms of ownership, usage, and social welfare. We find that the difference in social welfare between the profit-maximizing and social-welfare–maximizing platforms is relatively modest. The online appendix is available at https://doi.org/10.1287/mnsc.2017.2970 . This paper was accepted by Gad Allon, operations management.

Recommendation algorithms have acquired a central role in the suggestion of content within both subscription video on demand (SVOD) and advertising-based video on demand (AVOD) services and media-sharing platforms. In this article, we suggest the introduction of the datacasting paradigm, which takes into account the increasing relevance algorithms have in selection processes on audiovisual platforms. We use TikTok as a case study as it is an entirely algorithmic platform, and therefore embodies the heart of our discussion, and analyse how the algorithmic flow within the platform influences user experience, the impact it has on the enjoyment of content, and whether the platform can be considered televisual. We have opted to frame TikTok within debates on flow, as we believe that is what is at the core of the platform experience. Through the analysis of in-depth interviews, we extracted two main categories of responses: TV on TikTok and TikTok as TV. The former includes all responses related to the consumption of traditional televisual material on the platform, while the latter looks at all potential connections between the platform and television viewing habits.

Advanced wireless technology and smart mobile devices allow users to watch Internet video from almost anywhere. The major VOD platforms are competing with each other for customers, slowly shifting from a \"product-centric\" strategic goal to a \"customer-centric\" one. At present, existing research is limited to platform business model and development strategy as well as user behavior research, but there is less research on customer retention prediction. In order to effectively solve the customer retention prediction problem, this study applies machine learning methods to video-on-demand platform customer retention prediction, improves the traditional RFM model to establish the RFLH theoretical model for video-on-demand platform customer retention prediction, and uses machine learning methods to predict the number of customer retention days. The Optuna algorithm is used to determine the model hyperparameters, and the SHAP framework is integrated to analyze the important factors affecting customer retention. The experimental results show that the comprehensive performance of the LightGBM model is better than other models. The total number of user logins in the past week, the length of video playback in the same day, and the time difference between the last login and the present are important features that affect customer retention prediction. This study can help companies develop effective customer management strategies to maximize potential customer acquisition and existing customer retention for maximum market advantage.

Purpose - The literature review aims to facilitate a broader understanding of on-demand service platform operations management and proposes potential research directions for scholars. Design/methodology/approach - This study searches four databases for relevant literature on on-demand service platform operations management and selects 72 papers for this review. According to the research context, the literature can be divided into research on \"a single platform\" and research on \"multiple platforms\". According to the research methods, the literature can be classified into \"Mathematical Models\", \"Empirical Studies\", \"Multiple Methods\" and \"Literature Review\". Through comparative analysis, we identify research gaps and propose five future research agendas. Findings - This paper proposes five research agendas for future research on on-demand service platform operations management. First, research can be done to combine classic research problems in the field of operations management with platform characteristics. Second, both the dynamic and steady-state issues of on-demand service platforms can be further explored. Third, research employing mathematical models and empirical analysis simultaneously can be more fruitful. Fourth, more research efforts on the various interactions among two or more platforms can be pursued. Last but not least, it is worthwhile to examine new models and paths that have emerged during the latest development of the platform economy. Originality/value - Through categorizing the literature into two research contexts as well as classifying it according to four research methods, this article clearly shows the research progresses made so far in on-demand service platform operations management and provides future research directions.

PurposeFor an entrant platform in the on-demand service market, choosing an appropriate employment model is critical. This study explores how the entrant optimally chooses the employment model to achieve better performance and investigates the optimal pricing strategies and wage schemes for both incumbent and entrant platforms.Design/methodology/approachBased on the Hotelling model, the authors develop a game-theoretic framework to study the incumbent's and entrant's optimal service prices and wage schemes. Moreover, the authors determine the entrant's optimal employment model by comparing the entrant's optimal profits under different market configurations and analytically analyze the impacts of some critical factors on the platforms' decision-making.FindingsThis study reveals that the impacts of the unit misfit cost of suppliers or consumers on the pricing strategies and wage schemes vary with different operational efficiencies of platforms. Only when both the service efficiency of contractors and the basic employee benefits are low, entrants should adopt the employee model. Moreover, a lower unit misfit cost of suppliers or consumers makes entrants more likely to choose the contractor model. However, the service efficiency of contractors has nonmonotonic effects on the entrant's decision.Originality/valueThis study focuses on an entrant's decision on the optimal employment model in an on-demand service market, considering the competition between entrants and incumbents on both the supplier and consumer sides, which has not been investigated in the prior literature.

An increasing percentage of the population needs assistance services for a wide range of activities related to their independent living, which can be delivered either by humans or by machines. While cloud computing and emerging ICT solutions have introduced new types of services and service delivery paradigms, it remains costly and difficult to set up and market assistance services addressing the needs of very narrow end-user groups. The current work presents the design of a novel open-source infrastructure (web-based platform) which enables diverse stakeholders to easily set up web-based assistance-on-demand platform instances. Each of these AoD instances can be used by service providers to register and offer assistance-on-demand (AoD) services for catering individual needs of persons with disabilities. In this way, narrow end-user groups can be reached and enjoy a gamut of assistance services. The AoD platform is implemented in the context of a larger Global Public Inclusive Infrastructure (GPII), which is being developed in the context of the Prosperity4All project. The functional requirements for the design of the AoD infrastructure were iteratively fine-tuned through the definition and analysis of representative use models (sets of personas and use cases). The AoD platform architecture consists of two major components: front-end (GUI) and back-end (service infrastructure). Their implementation is mainly based on the Django framework which enables a Model–Template–View (MTV) software architectural pattern with highly configurable and flexible components. The evaluation of a demonstration instance of the AoD platform with end-users identified its advantages and pointed out additional functionality needed: multi-language, multi-modality, embedded social media feeding, dedicated menu for highly flexible service, etc. The overall perceived usefulness of the specific demonstration AoD platform instance that underwent evaluation was higher than 80%. The proposed AoD platform provides a flexible and sufficiently generic web-based infrastructure for the cost-effective setup, registration and web publication of services that can accommodate a highly diverse range of assistance needs. Furthermore, it is suitable for use by a wide range of different stakeholder/user groups interested in addressing the needs of persons with disabilities or otherwise at risk of exclusion.

With a burgeoning market and a multitude of on-demand food delivery (OFD) platforms offering diverse options, comprehending the reasons that drive consumers to switch between platforms is paramount. The push-pull-mooring (PPM) theory provides a comprehensive framework for assessing why and how consumers navigate, guiding strategic decisions for service providers seeking to optimize their offerings and retain their customer base. This research employs the PPM theory to rigorously analyze how these elements influence consumers’ intentions to switch between OFD platforms in Taiwan. Findings from a comprehensive survey of 441 OFD users reveal that both pull and mooring factors exert a significant influence on consumers’ inclination to switch platforms, collectively explaining about 42% of the switching intention. Recognizing these critical factors empowers managers to make judicious decisions aimed at enhancing platform offerings and refining marketing strategies, ultimately fortifying customer retention and bolstering satisfaction levels.

Recommendation System is one of such solutions to overcome information overload issues and to identify products most relevant to users and provide suggestions to users for items they might be interested in consuming or elements matching their needs. The significant challenge of several recommendation approaches is that they suggested a huge number of things to the target user. But the exciting items, according to the target user, are seen at the bottom of the recommended list. The proposed approach has improved the quality of recommendations by implementing some of the unique features in the new framework of auto encoder called semi-autoencoder, which contains the rating information as well as some additional information of users. Autoencoder is widely used in the recommender system because it gives the best result for feature extraction, dimensionality reduction, regeneration of data, and a better understanding of the user’s characteristics. The experimental results are compared with some established popular methods using precision, recall, and F-measure evaluation measures. Users generally don’t want to see lots of suggestions. With its six building blocks, the proposed approach gives better performance for the top 10 recommendations compared to other well-known methods.

The availability of videos has grown rapidly in recent years. Finding and browsing relevant information to be automatically extracted from videos is not an easy task, but today it is an indispensable feature due to the immense number of digital products available. In this paper, we present a system which provides a process to automatically extract information from videos. We describe a system solution that uses a re-trained OpenNLP model to locate all the places and famous people included in a specific video. The system obtains information from the Google Knowledge Graph related to relevant named entities such as places or famous people. In this paper we will also present the Automatic Georeferencing Video (AGV) system developed by RAI (Radiotelevisione italiana, which is the national public broadcasting company of Italy, owned by the Ministry of Economy and Finance) Teche for the European Project “La Città Educante” (The Educating City: teaching and learning processes in cross-media ecosystem) Our system contributes to The Educating City project by providing the technological environment to create statistical models for automatic named entity recognition (NER), and has been implemented in the field of education, in Italian initially. The system has been applied to the learning challenges facing the world of educational media and has demonstrated how beneficial combining topical news content with scientific content can be in education.

Dynamic adaptive streaming over HTTP (DASH) is the crucial factor in the rapid penetration of over-the-top (OTT) service providers for on-demand video streaming. It can also be used for live video streaming by the OTT providers. The recent advancements of the HTTP chunked transfer, and the Common Media Application Format (CMAF) echo this tendency, which introduces the possibility to deliver a video segment by small chunks before the full segment is generated. It can deliver live latency of three seconds or less on a conventional DASH player with a small buffer capacity less than the target live latency. However, legacy bitrate adaptation mechanisms inaccurately measure the available bandwidth due to idle times between the chunks at the encoder side. To resolve this problem, we utilize the Software-Defined Networking (SDN) paradigm that directly provides the network statistics with the available bandwidth. We, then, propose an SDN-assisted bitrate adaptation mechanism for live streaming with HTTP 1.1 Chunked Transfer of CMAF packages while keeping the coexistence with the legacy DASH clients. Our SDN-based central framework asynchronously sends the video bitrate levels by continuously monitoring the background traffic flows and the available capacity for DASH clients on the same shared bottleneck link. Results show that our proposed mechanism achieves a lower video freeze rate and provides a better quality-of-experience while reducing the live latency down to about three seconds in the existence of varying background traffic.